Multi-layer system optimization

ABSTRACT

A software-defined network multi-layer controller (SDN-MLC) may communicate with multiple layers of a telecommunication network. The SDN-MLC may have an optimization algorithm that helps manage, in near real-time, the multiple layers of the telecommunication network.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. NonProvisional patentapplication Ser. No. 16/022,212, filed on Jun. 28, 2018, entitled“Multi-Layer System Optimization,” the contents of which are herebyincorporated by reference herein.

BACKGROUND

A packet layer of the network may include internet protocol (IP) linksconnected among IP devices such as router ports. The IP links may berouted over a path in the optical layer and use reconfigurable opticaladd-drop multiplexers (ROADMs) and transponders at the endpoints, andoptical signal regenerators (or repeaters) in the middle of the pathwhen the path is too long. IP ports, optical transponders, and opticalregenerators are typically associated with a certain bandwidth unit suchas 40 Gbps, 100 Gbps, 200 Gbps, or 400 Gbps. If there are N trafficendpoints and K Quality of Service (QoS) classes then the traffic matrixconsists of K*N*(N−1) individual traffic units, all of which may changeover time. This disclosure is directed to addressing proactiveconfiguration changes with regard to different layers of the network.

SUMMARY

Disclosed herein are techniques that may address repeated joint globaloptimization (e.g., whenever network condition changes) while running amulti-layer network. These network condition changes may be based onscheduled outages (e.g., maintenance activity such as software upgrades)or unscheduled outages (e.g., caused by fiber cuts or failure of IP oroptical devices). A software-defined network multi-layer controller(SDN-MLC) may communicate with multiple layers of a telecommunicationnetwork, which may include dynamic fiber cross connect equipment inorder to implement needed network configurations.

The SDN-MLC may have an optimization algorithm that helps manage, innear real-time, the multiple layers of the telecommunication network.Joint multi-layer global optimization may be used to respond to networkcondition changes caused by traffic matrix changes, scheduled outages,or unscheduled outages.

In an example, an apparatus (e.g., software-defined network controller)may include a processor and a memory coupled with the processor thateffectuates operations. The operations may include: obtaining, by adevice, multiple layer information associated with multiple layers of atelecommunications network, the multiple layer information may compriseoptical layer information or router layer information; forecasting anetwork condition based on machine learning that uses the multiple layerinformation; based on the forecasting, determining that the networkcondition may be avoided based on configuration changes of thetelecommunications network; and based on the determining of theconfiguration changes, sending, by the device, a message to anotherdevice in order to instruct it to automatically implement theconfiguration changes.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter. Furthermore,the claimed subject matter is not limited to limitations that solve anyor all disadvantages noted in any part of this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale.

FIG. 1A illustrates an exemplary system for managing multi-layerself-optimization.

FIG. 1B illustrates FIG. 1A in further detail.

FIG. 2 illustrates an exemplary method for managing multi-layer systemself-optimization.

FIG. 3 illustrates an exemplary method for managing multi-layer systemself-optimization.

FIG. 4 illustrates an exemplary method for multi-layer systemoptimization as disclosed herein.

FIG. 5 illustrates an exemplary system for multi-layer optimization.

FIG. 6 illustrates an exemplary method for multi-layer optimization.

FIG. 7 illustrates a schematic of an exemplary network device.

FIG. 8 illustrates an exemplary communication system that provideswireless telecommunication services over wireless communicationnetworks.

FIG. 9 is a representation of an exemplary network.

DETAILED DESCRIPTION

Conventional approaches may assume that the mapping between an IP linkand the set of optical transponders and regenerators needed underneathis fixed and if any component fails, the entire IP link fails and thenon-failed components of the IP link are rendered unusable. Also,conventional approaches may assume consideration of traffic routing overthe IP layer and the optical layer separately. Conventionally, opticallayer optimization (e.g., the choice of IP links and their mapping overthe optical layer) may rarely be done (e.g., once) and when opticallayer optimization is done it is usually with a consideration that theIP layer traffic should only be routed over these once-determined set ofIP links. Again, due to lack of joint global optimization of IP andoptical layers, conventional approaches used significantly high IPresources and optical resources.

Disclosed herein are techniques that may address repeated joint globaloptimization (whenever network condition changes) while operating amulti-layer network. These network condition changes may be based onscheduled outages (e.g., maintenance activity such as softwareupgrades), unscheduled outages (e.g., caused by fiber cuts or failure ofIP or optical devices), or planned or unplanned traffic changes (e.g.,spike in traffic to an internet resource because of an emergency orwebsite promotion). When the unscheduled outages or traffic changes areencountered, the global optimization may take place in real-time.

FIG. 1A illustrates an exemplary system for near real-timeself-optimization. Software-defined network multi-layer controller 112(SDN-MLC 112) may communicate with multiple layers of system 100 (e.g.,a telecommunication network). SDN-MLC 112 may have an optimizationalgorithm that helps manage multiple layers of the network. The multiplelayers may include optical layer 150, router layer 130 (which may alsobe a switch layer), and multi-protocol label switching (MPLS) tunnelinglayer 120. As generally shown in FIG. 1A (and with more detail in FIG.1B), there may be multiple sites, which include one or more componentsthat help build one or more physical or logical connections. Forexample, in FIG. 1B, for site 101 it may include router 131, tail 161,ROADM 151, or regenerator 171 of FIG. 1B). FIG. 1A illustratesconnections between multiple sites as may be seen at each level. Sitesinclude site 101 through site 109. Some sites (e.g., site 107) may haveoptical equipment (ROADM 157), but may not have routing equipment.

FIG. 1B illustrates FIG. 1A in further detail. Optical layer 150 mayinclude multiple components, which are optional based on circumstance,such as reconfigurable optical add-drop multiplexer (ROADMs) (e.g.,ROADM 151-ROADM 159), tails (e.g., tail 161-tail 168), and regenerators(e.g., regenerator 171-regenerator 178). A ROADM is a form of opticaladd-drop multiplexer that adds the ability to remotely switch trafficfrom a wavelength-division multiplexing (WDM) system at the wavelengthlayer. A tail is a connection between an internet protocol (IP) port(e.g., port of router 131) and a transponder port (e.g., port oftransponder 181). In optical fiber communications, a transponder may bethe element that sends and receives the optical signal from a fiber. Atransponder is typically characterized by its data rate and the maximumdistance the signal can travel. An optical communications regeneratormay be used in a fiber-optic communications system to regenerate anoptical signal. Such regenerators may be used to extend the reach ofoptical communications links by overcoming loss due to attenuation ofthe optical fiber. Some regenerators may also correct for distortion ofthe optical signal by converting it to an electrical signal, processingthat electrical signal, and then retransmitting an optical signal.Router layer 130 may include routers (e.g., router 131-router 136) orswitches (not shown). MPLS layer 120 may have several tunnels logicallyconnected via the routers in router layer 130.

FIG. 2 illustrates an exemplary method for near real-timeself-optimization. In an exemplary scenario, as shown in FIG. 1A andFIG. 1B, there may be a system 100 with an optical layer 150, routerlayer 130, and a MPLS layer 120. At step 191, information about eachlayer may be obtained, which may be over seconds, minutes, days, weeks,months, or more. This information may be before, during, or subsequentto an outage or other event of system 100 and may be used forforecasting. The information may assist in understanding activitypatterns for system 100. For example, activity patterns (which mayinclude scheduled or unscheduled outages) may include the frequency ofnetwork link outages (and flow of traffic activity thereafter), datesand times of significant traffic load on system 100, minimum or maximumaverage (or median) traffic load on system 100 during a period, trafficmatrix changes, trouble tickets (outages may be experienced by users,but not detected through normal techniques), or estimated time of repairon a layer (which may be based on similar errors, alarms, or diagnosedissues), among other things. Information may be gathered on each layer.For MPLS layer 120, the information may include the MPLS interfacestate, reserved bandwidth, or label switching paths, among other things.For router layer 130, there may be information that includes inputbytes, output bytes, input packets, output packets, input errors, inputdrops, input framing errors, output errors, output drops, usual trafficload on affected link, types of traffic on affected link (e.g., definedQoS, video, voice, TCP, UDP, source address, etc.), or routinginformation, among other things. The router layer information may beobtained from one or more routers. For optical layer 150, theinformation may include location of optical equipment (e.g., transponder181, regen 171, ROADM 157, or tails), errors from the optical equipment,length of optical paths, or outages of the optical equipment, amongother things. The optical layer information may be obtained from onemore ROADMs.

With continued reference to FIG. 2, based on multi-layer information ofstep 192 (e.g., optical layer and router layer information), there is adetermination whether a change short-term or intermediate-term change inconfiguration of existing capacity of the optical layer or router layermay resolve undesirable network conditions (e.g., anticipatedperformance problems, such as dropped packets, low bandwidth, etc.)alerted to by the information (e.g., information of step 191). Aresolution may be based on a predetermined period and predeterminedengineering (or the like) thresholds. The period would usually rangefrom days to weeks or months. The predetermined engineering thresholdsmay include service level agreements (SLAs) for transport for much ofthe information of step 191, such as errors, traffic loads, or types oftraffic, among other things. For example, in this scenario, the loadalong an optical path site 101-107-106-104 (i.e., path 11) may havereached a threshold (e.g., 80 percent) during a period (e.g., 30 minutetime frame), which may cause errors or latency. A first selected optionmay be for ROADMs along the path (e.g., ROADM 151, ROADM 157, ROADM 156,and ROADM 154) to add one more wavelengths carrying data channels toincrease the capacity along path 11. This may be preferred over changinga routing path at routing layer 130, because there may be a tendency forthe routing protocol to send traffic through router path site 105-104,which actual goes over optical path site 106-104 and doesn't helpresolve the congestion. Or alternatively, it may be selected becauseSDN-MLC 112 may constantly have to adjust the routing metrics, which maycause high utilization (e.g., eventually slow response) of SDN-MLC 112,or it may be selected because routing changes would have a service falloutside its SLA.

A second selected option (at a different time with different weightedinfo) may be for just the router layer 120 to be changed. Routes to someor all the traffic may be weighted to go through one or more routers(e.g., router 132) of site 102, because optical path site102-103-109-104 does not go through optical path site 106-104. Anadditional consideration that may have led for this second selectionoption may be that the ROADMs could not (e.g., no more channelsavailable) or should not increase its wavelength based on theinformation as disclosed in step 191. A third selected option (at yet adifferent time with different weighted info) may decide to do acombination of router layer 130 and optical layer 150 solutions (e.g.,configuration changes) in order to reduce the traffic to an acceptablethreshold (e.g., 30 percent). For instance, traffic for a couple ofheavy users along the path may be redirected and stay within SLAs, whilean increase in ROADM may be enough to accommodate the traffic load basedon information of other users. SDN-MLC 112 may be used to determine thechange needed in this step 192.

With reference to step 192, it may have sub-divided steps forforecasting (e.g., proactive network changes). For example, there may bea determination of somewhat immediate (e.g., near real-time) changes toconfigurations that may be communicated to router layer 130 or opticallayer 150 devices. There may also be a consideration of the differentparts within system 100 (e.g., see FIG. 3) that may not be automaticallyor immediately changed, but would provide optimization (for example)that would address issues determined by analyzing the information ofstep 191 or the like. As shown, SDN-MLC 112 may obtain data fromdifferent sources, such as tail database 114, which allows SDN-MLC 112to know the current usage of router layer 130 components or opticallayer 150 components. If a regenerator, ROADM, tail, or the like are notin use (e.g., because of reprovisioned customer service) or can be addedto system 100 to extend the period to be within the predeterminedengineering thresholds, then SDN-MLC may automatically order thecomponent or provide an alert (e.g., via a display) regarding thecomponent to be authorized and order by a user.

With continued reference to step 192, as disclosed herein, optimalestimate may consider the lowest capital cost but yet the estimation maysatisfy both no-fail scenario and a specified set of failure scenariosand under each such scenario it should satisfy a specified set ofengineering rule constraints (e.g., percentage of traffic of each typeto be carried and latency constraints). Again, due to lack of jointglobal optimization of IP and optical layers, conventional approachesused significantly more IP resources and optical resources. It iscontemplated that step 192 may be used for immediate problem (e.g.,outage) resolution.

At step 193, SDN-MLC 112 may provide instructions based on thedetermination of step 192. For example, SDN-MLC 112 may communicate withrouters, ROADMs, tails, ordering system 115, ROADM SDN controller 110,or the like to execute the determination of step 192. ROADM SDNcontroller 110 may be an intermediate device that may directlycommunicate with optical layer 150 devices. Ordering system 115 may beused to order one or more devices for future use (e.g., spare). Theremay be an anticipated need for the spare based on the information ofstep 191. At this step, SDN-MLC 112 may display options for system 100that is acted on by a user or provide instructions for a near-real timeautomatic change in telecommunication network operation that addressesthe immediate problem.

SDN-MLC 112 may manage the multiple layers of system 100 in a closedloop and heuristic manner. In a first example, this management may allowfor dynamic mapping between a router layer and an optical layer by usingcolorless or directionless open ROADMs and reusing non-failed routerlayer or optical layer components of a failed link. In a second example,this management may allow for the use of spare tails (connection betweena router port and optical transponder port) and spare opticalregenerators. In a third example, this management by SDN-MLC 112 mayallow router layer devices to be physical or virtual and software andhardware to be aggregated (e.g., traditional routers) or dis-aggregated(e.g., whitebox switches).

Based on the network condition (e.g., traffic matrix or outages)changes, the mapping between IP (e.g., router) and optical layers may bechanged to more efficiently carry traffic under the changed networkcondition. Joint optimization of IP and optical layers whenever thenetwork condition changes may be done by using algorithms based oninteger linear programming or heuristics.

FIG. 3 illustrates an exemplary method for managing multi-layer systems(e.g., near real-time optimization) as disclosed herein. At step 201,initially system 100 may include unconnected sets of tails andregenerators. Each such tail and optical regenerator may be classifiedas spare tails and spare optical regenerators. The classification andthe control of the devices (e.g., tail and optical regenerator) can bedone automatically. For a given network condition, only a selectedsubset of spare tail pairs may be connected to form an IP link thatcarries the required traffic (e.g., less than 70 percent load) under therequired latency constraints (e.g., 5 ms). Depending on the length(e.g., in miles) of a specific IP link, it may require spare opticalregenerators as well. At step 202, a joint multi-layer globaloptimization may be done to choose the right set of spare tail pairs(plus optical regenerators, if needed) along with the proper IP layerrouting. The joint optimization should satisfy engineering ruleconstraints (e.g., percentage of traffic of each type to be carried andlatency constraints), use realizable routing (e.g., shortest pathrouting, constrained shortest path routing, multi-commodity flow routingwith the restriction of equal splitting of traffic units, etc.), oroptimize some other desirable quantities (e.g., maximizing unused sparetails and regenerators, minimizing the maximum traffic on a link, etc.).At step 203, SDN-MLC 112 may provide instructions for IP devices (e.g.,physical or virtual routers) or optical layer devices to be turned up ordown depending on network conditions of system 100.

With continued reference to FIG. 3, at step 204, there may be a detectedchange in network condition that may result in a change in the trafficmatrix and a certain set of tails and regenerators to fail, oralternatively some previously failed tails and regenerators may becomeoperational. At step 205, taking account of the traffic and failureconditions, a new joint multi-layer global optimization may beperformed. The new joint multi-layer global optimization may result insome previously established IP links to be taken down and some new IPlinks to be added and thus also reflecting the dynamic nature andcontrol of the network topology. The joint global optimization problemmay be formulated as an exact integer linear programming problem, but ifthe exact algorithm is time consuming (e.g., hours) then a fastheuristic, which may take seconds or minutes, may be used. At step 206,SDN-MLC 112 may provide instructions for IP devices (e.g., physical orvirtual) or optical layer devices to be turned up or down depending onnetwork conditions of system 100. Software or hardware may remainaggregated or may be disaggregated.

FIG. 4 illustrates another exemplary method for near real-timeoptimization as disclosed herein. The main capacity units used tooptimize over are the tails and regenerators where a tail includes an IPport and a transponder. Let C1 and C2 represent the costs of a tail anda regenerator respectively, and N1 and N2 represent the numbers of tailsand regenerators needed in the final solution. The quantity(C1*N1+C2*N2) is optimized over the possible network change scenarios.At step 211, the number of tails are set to zero. Initially the networkmay only include zero tails and zero regenerators. At step 212, for onegiven network change condition, minimum set of tails and regeneratorsmay be selected that can be connected appropriately to form IP linksthat can carry the required traffic under the required engineering ruleconstraints. At step 213, the analysis of every network change conditionmay be repeated. This selection may be based on analysis of manydifferent traffic scenarios and different failure scenarios. Preferably,all the different combination are analyzed. For example, repeated may besimilar to the following: 1). Peak traffic on day 1; 2) Peak traffic onday 2; 3) Day 1, but some of the links/routers have failed—with sametraffic load for day 1, etc. For any given network change condition,tails and regenerators used for previous network change conditions maybe considered for reuse. The joint global optimization problem over theseveral network change scenarios can be formulated as an exact integerlinear programming problem, but if the exact algorithm is time consumingthen a fast heuristic may be used. At step 214, SDN-MLC 112 implementsor provides options (e.g., displays) for implementing one or moreimmediate changes.

The disclosed near real-time optimization approach may anticipate allfailure and traffic surge scenarios over the next few weeks or monthsand for each scenario may consider a joint router layer or optical layerglobal optimization to minimize the total cost of tails, regenerators,or other devices. The disclosed methods, systems, and apparatuses mayprovide an optimal network that results in network capex savings in therange of 15-30% or more. Table 1 helps illustrate an example with thecomponents of cost that may include router IP port, optical transponder,or optical regenerator. For this example, note that Tail=PacketPort+Transponder; traffic Numbers˜YE'20 with current engineering rulesassumed; Cost assumptions are: 1) optical regeneratorcost˜1.5*Transponder cost; and 2) router port cost ˜2.5*Transpondercost. The normalized cost is in units of transponder cost for thisexample. As shown, there is potential for ˜34% CAPEX cost saving withjoint global optimization.

TABLE 1 # of 100 # of 100 Normalized Scenario GE Tails GE Regens CostPMO 1153 226 2105 +Fast Provisioning 1048 105 1913 (−9%) +JointOptimization  939  91 1711 (−18.5%) on Existing Links +Joint Global  740130 1392 (−34%) Optimization (add links anywhere)

FIG. 5 illustrates an exemplary system for multi-layer systemoptimization. As shown, there may be SDN-MLC 112 that communicates withoptical layer 150 devices (e.g., transponder 225, transponder 226, ROADM227, ROADM 228, transponder 229, or DFCC 231) and router layer 130devices, such as port 221-port 224 or router 131. Port 221-port 224 forsimplicity are considered as different ports off of one router 131, butmay represent ports across multiple routers. SDN-MLC 112 may communicateto optical layer (e.g., layer zero) via an optical layer SDN controller(herein known as L0 SDN controller 116). DFCC 231 is a Dynamic FiberCross Connect that allows for automatically switching between ports andtransponders, as presented. It is contemplated herein that other devicesthat may use an automatic switching function may be used herein.

The disclosed configuration, as shown in FIG. 5, further assists withframework for a tight closed loop control of a multi-layerpacket/optical network operation. Forecasts, as disclosed herein, ofnetwork conditions (traffic matrix and outages) may be based on machinelearning approaches, knowledge of future maintenance events, knowledgeof future bandwidth-calendaring commitments, or near real-timemonitoring of traffic and failures. The machine learning approach canpredict traffic or outage at a future point of time by observing massiveamount of traffic and outage data in the past and applying artificialintelligence and computational statistical techniques. Thebandwidth-calendaring provides bandwidth to users over a certain pairsof sources and destinations in the network over a certain future periodof time. Based on past observation, the network operator knows when andwhere spare bandwidth will be available and schedules newbandwidth-calendaring requests to optimally fill in the gaps. DFCC 231facilitates decoupling of components of a tail (e.g., router port andoptical transponder) and dynamically connecting the components of thetail whenever needed. Router 131 may have a spare router port 223 andthere may be spare transponders. The DFCC device may dynamically connecta spare router port 223 to a spare optical transponder (e.g.,transponder 225 or transponder 226) to create a tail. If the need is nolonger there, then the connection may be severed and the router port 223and the spare optical transponder may go back to a spare pool.Furthermore, if the components of a tail fails, the other component maystay operational and may be able to form a new Tail by connecting with aspare complementary component. This function may assist in a moreefficient network bandwidth usage and allow for a more optimal bandwidthon demand or bandwidth calendaring service. As an example, consider alocation with two different router ports R1 and R2 and one transponderT. Initially the DFCC is used to create a Tail by connecting R1 and T.This Tail in turn can be connected with another Tail (and 0 or moreregens) to create an end-to-end IP link. Now suppose the router port R1fails. Normally that means both the Tail and the associated end-to-endIP link will also fail. However, using the DFCC we can remove theconnection between R1 and T and instead create a new Tail by connectingR2 with T. Using this new Tail, we can create another end-to-end IPlink. If we did not have DFCC, we would have had to provision a newTail, which would have been expensive. In addition, depending on abandwidth-calendaring service need, it may be more useful to have thetail starting from router port R1 or router port R2 and the DFCC devicecan make the appropriate switch.

FIG. 6 illustrates an exemplary method for tail configuration in asoftware-defined network. In an exemplary scenario, initially, port 221may connect through DFCC 231 to transponder 225 and port 222 may connectthrough DFCC 231 to transponder 226. Port 223 may be considered a spareport that may not connect with a transponder. At step 241, informationmay be obtained about system 100. The obtained information may includeinformation associated with transponder 225, transponder 226, or port221 through port 223. The information may include whether transpondersare active (e.g., being used), whether transponders are functional(e.g., in working condition), locations of transponders in connectionwith DFCC 231, or status of transponder as a spare. The aforementionedtransponder information may apply to information associated with arouter port. Other information as discussed herein may also be obtained.At step 242, there may be a determination that there is an undesirablenetwork condition (e.g., link down or error). This determination may bebased on the information of step 241.

At step 243, based on the determining of the undesirable networkcondition of step 242, there may be a determination that a second tailreconfiguration (e.g., port 223-to-transponder 225) may satisfactorilyresolve the undesirable network condition associated with a first tailconfiguration (e.g., port 221-to-transponder 225). Associated with thisstep 243, there may be a determination of which ports and transpondersare usable. If a port or transponder is not usable (to resolve theundesirable network condition) then DFCC 231 may be used to switch tothe respective usable port or transponder. So in this scenario, port 223may be used in place of port 221, while the same transponder 225 may beused. In this situation, SDN-MLC 112 may also need to send instructionsto router 131 to reconfigure port 223 to duplicate most or all of theport configurations that were on port 221. Configurations may includeinternet protocol addresses or tunneling configurations among otherthings.

At step 224, there may be a message sent (e.g., by SDN-MLC 112 or L0 SDNcontroller 116) to switch to the determined second tail reconfigurationof step 223. Subsequent to the switch, the tail database 114 may beupdated. It is contemplated that other changes to system 100 may be made(e.g., similar to configurations in step 192). It is furthercontemplated herein that the steps and configurations as describedthroughout may be used in combination or out of order of the stepspresented.

Although a router layer, optical layer, and MPLS tunneling layer arediscussed, it is contemplated that the MPLS tunneling layer may be someother tunneling layer or not present at all. Also, it is contemplatedthat the optical layer may be another physical layer other than optical.As discussed herein, the router layer, may be a switching layer or thelike. It is contemplated herein that the term information as consideredherein may be information on any layer (e.g., layer 130 or layer 150).Activity patterns as disclosed herein may be considered “information”which is used in step 192. Whether an outage is scheduled or unscheduledis other information that may be used for determining near real-timeoptimization solutions. With reference to estimated time for repair, itis contemplated that sometimes it may take less time to implement arouter layer solution rather than an optical layer solution (or viceversa). Although time may be a significant factor, SDN-MLC 112 mayconsider a predetermined weight of the information in order to derive aweighted determination (e.g., step 192 or step 202). The disclosedtechniques may be used to help change the configuration of devices indifferent layers of the network so that near real-time optimization maymeet the quality of service requirements of the network.

FIG. 7 is a block diagram of network device 300 that may be connected toor comprise a component of system 100. Network device 300 may comprisehardware or a combination of hardware and software. The functionality tofacilitate telecommunications via a telecommunications network mayreside in one or combination of network devices 300. Network device 300depicted in FIG. 7 may represent or perform functionality of anappropriate network device 300, or combination of network devices 300,such as, for example, a component or various components of a cellularbroadcast system wireless network, a processor, a server, a gateway, anode, a mobile switching center (MSC), a short message service center(SMSC), an automatic location function server (ALFS), a gateway mobilelocation center (GMLC), a radio access network (RAN), a serving mobilelocation center (SMLC), or the like, or any appropriate combinationthereof. It is emphasized that the block diagram depicted in FIG. 7 isexemplary and not intended to imply a limitation to a specificimplementation or configuration. Thus, network device 300 may beimplemented in a single device or multiple devices (e.g., single serveror multiple servers, single gateway or multiple gateways, singlecontroller or multiple controllers). Multiple network entities may bedistributed or centrally located. Multiple network entities maycommunicate wirelessly, via hard wire, or any appropriate combinationthereof.

Network device 300 may comprise a processor 302 and a memory 304 coupledto processor 302. Memory 304 may contain executable instructions that,when executed by processor 302, cause processor 302 to effectuateoperations associated with mapping wireless signal strength. As evidentfrom the description herein, network device 300 is not to be construedas software per se.

In addition to processor 302 and memory 304, network device 300 mayinclude an input/output system 306. Processor 302, memory 304, andinput/output system 306 may be coupled together (coupling not shown inFIG. 7) to allow communications between them. Each portion of networkdevice 300 may comprise circuitry for performing functions associatedwith each respective portion. Thus, each portion may comprise hardware,or a combination of hardware and software. Accordingly, each portion ofnetwork device 300 is not to be construed as software per se.Input/output system 306 may be capable of receiving or providinginformation from or to a communications device or other network entitiesconfigured for telecommunications. For example input/output system 306may include a wireless communications (e.g., 3G/4G/GPS) card or wiredcommunications (e.g., optical lines) card. Input/output system 306 maybe capable of receiving or sending video information, audio information,control information, image information, data, or any combinationthereof. Input/output system 306 may be capable of transferringinformation with network device 300. In various configurations,input/output system 306 may receive or provide information via anyappropriate means, such as, for example, optical means (e.g., infrared),electromagnetic means (e.g., RF, Wi-Fi, Bluetooth®, ZigBee®), acousticmeans (e.g., speaker, microphone, ultrasonic receiver, ultrasonictransmitter), or a combination thereof.

Input/output system 306 of network device 300 also may contain acommunication connection 308 that allows network device 300 tocommunicate with other devices, network entities, or the like.Communication connection 308 may comprise communication media.Communication media typically embody computer-readable instructions,data structures, program modules or other data in a modulated datasignal such as a carrier wave or other transport mechanism and includesany information delivery media. By way of example, and not limitation,communication media may include wired media such as a wired network ordirect-wired connection, or wireless media such as acoustic, RF,infrared, or other wireless media. The term computer-readable media asused herein includes both storage media and communication media.Input/output system 306 also may include an input device 310 such askeyboard, mouse, pen, voice input device, or touch input device.Input/output system 306 may also include an output device 312, such as adisplay, speakers, or a printer.

Processor 302 may be capable of performing functions associated withtelecommunications, such as functions for processing broadcast messages,as described herein. For example, processor 302 may be capable of, inconjunction with any other portion of network device 300, determining atype of broadcast message and acting according to the broadcast messagetype or content, as described herein.

Memory 304 of network device 300 may comprise a storage medium having aconcrete, tangible, physical structure. As is known, a signal does nothave a concrete, tangible, physical structure. Memory 304, as well asany computer-readable storage medium described herein, is not to beconstrued as a signal. Memory 304, as well as any computer-readablestorage medium described herein, is not to be construed as a transientsignal. Memory 304, as well as any computer-readable storage mediumdescribed herein, is not to be construed as a propagating signal. Memory304, as well as any computer-readable storage medium described herein,is to be construed as an article of manufacture.

Memory 304 may store any information utilized in conjunction withtelecommunications. Depending upon the exact configuration or type ofprocessor, memory 304 may include a volatile storage 314 (such as sometypes of RAM), a nonvolatile storage 316 (such as ROM, flash memory), ora combination thereof. Memory 304 may include additional storage (e.g.,a removable storage 318 or a non-removable storage 320) including, forexample, tape, flash memory, smart cards, CD-ROM, DVD, or other opticalstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, USB-compatible memory, or any othermedium that can be used to store information and that can be accessed bynetwork device 300. Memory 304 may comprise executable instructionsthat, when executed by processor 302, cause processor 302 to effectuateoperations to map signal strengths in an area of interest.

FIG. 8 depicts an exemplary diagrammatic representation of a machine inthe form of a computer system 500 within which a set of instructions,when executed, may cause the machine to perform any one or more of themethods described above. One or more instances of the machine canoperate, for example, as processor 302, router 131, and other devices ofFIG. 1A, FIG. 1B, and FIG. ZZ2. In some embodiments, the machine may beconnected (e.g., using a network 502) to other machines. In a networkeddeployment, the machine may operate in the capacity of a server or aclient user machine in a server-client user network environment, or as apeer machine in a peer-to-peer (or distributed) network environment.

The machine may comprise a server computer, a client user computer, apersonal computer (PC), a tablet, a smart phone, a laptop computer, adesktop computer, a control system, a network router, switch or bridge,or any machine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. It will beunderstood that a communication device of the subject disclosureincludes broadly any electronic device that provides voice, video ordata communication. Further, while a single machine is illustrated, theterm “machine” shall also be taken to include any collection of machinesthat individually or jointly execute a set (or multiple sets) ofinstructions to perform any one or more of the methods discussed herein.

Computer system 500 may include a processor (or controller) 504 (e.g., acentral processing unit (CPU)), a graphics processing unit (GPU, orboth), a main memory 506 and a static memory 508, which communicate witheach other via a bus 510. The computer system 500 may further include adisplay unit 512 (e.g., a liquid crystal display (LCD), a flat panel, ora solid state display). Computer system 500 may include an input device514 (e.g., a keyboard), a cursor control device 516 (e.g., a mouse), adisk drive unit 518, a signal generation device 520 (e.g., a speaker orremote control) and a network interface device 522. In distributedenvironments, the embodiments described in the subject disclosure can beadapted to utilize multiple display units 512 controlled by two or morecomputer systems 500. In this configuration, presentations described bythe subject disclosure may in part be shown in a first of display units512, while the remaining portion is presented in a second of displayunits 512.

The disk drive unit 518 may include a tangible computer-readable storagemedium 524 on which is stored one or more sets of instructions (e.g.,software 526) embodying any one or more of the methods or functionsdescribed herein, including those methods illustrated above.Instructions 526 may also reside, completely or at least partially,within main memory 506, static memory 508, or within processor 504during execution thereof by the computer system 500. Main memory 506 andprocessor 504 also may constitute tangible computer-readable storagemedia.

FIG. 9a is a representation of an exemplary network 600 (e.g., cloud).Network 600 (e.g., system 100) may comprise an SDN—that is, network 600may include one or more virtualized functions implemented on generalpurpose hardware, such as in lieu of having dedicated hardware for everynetwork function. That is, general purpose hardware of network 600 maybe configured to run virtual network elements to support communicationservices, such as mobility services, including consumer services andenterprise services. These services may be provided or measured insessions.

A virtual network functions (VNFs) 602 may be able to support a limitednumber of sessions. Each VNF 602 may have a VNF type that indicates itsfunctionality or role. For example, FIG. 9a illustrates a gateway VNF602 a and a policy and charging rules function (PCRF) VNF 602 b.Additionally or alternatively, VNFs 602 may include other types of VNFs.Each VNF 602 may use one or more virtual machines (VMs) 604 to operate.Each VM 604 may have a VM type that indicates its functionality or role.For example, FIG. 9a illustrates a management control module (MCM) VM604 a, an advanced services module (ASM) VM 604 b, and a DEP VM 604 c.Additionally or alternatively, VMs 604 may include other types of VMs.Each VM 604 may consume various network resources from a hardwareplatform 606, such as a resource 608, a virtual central processing unit(vCPU) 608 a, memory 608 b, or a network interface card (NIC) 608 c.Additionally or alternatively, hardware platform 606 may include othertypes of resources 608.

While FIG. 9a illustrates resources 608 as collectively contained inhardware platform 606, the configuration of hardware platform 606 mayisolate, for example, certain memory 608 c from other memory 608 c.

As described herein, a telecommunications system wherein management andcontrol utilizing a software designed network (SDN) and a simple IP arebased, at least in part, on user equipment, may provide a wirelessmanagement and control framework that enables common management andcontrol, such as mobility management, radio resource management, QoS,load balancing, etc., across many technologies; decoupling the mobilitycontrol from data planes to let them evolve and scale independently;reducing network state maintained in the network based on user equipmenttypes to reduce network cost and allow massive scale; shortening cycletime and improving network upgradability; flexibility in creatingend-to-end services based on types of user equipment and applications,thus improve customer experience; or improving user equipment powerefficiency and battery life—especially for simple M2M devices—throughenhanced wireless management.

Crossing or meeting a threshold as discussed herein, which may triggerthe determining step 192, may be described as surpassing a number thatis prescribed in order to determine when some action is triggered. Forexample, a threshold may be crossed if the number of keepalives from adevice is below a certain amount (e.g., 3) within a timeframe (e.g., 10minutes) and therefore an alert may be triggered. In another example, athreshold may be crossed if the number of errors is above a certainamount (e.g., 100) within a certain time frame (e.g., 1 minute) andtherefore an alert may be triggered.

While examples of a telecommunications system in which multi-layerself-optimization may be processed and managed have been described inconnection with various computing devices/processors, the underlyingconcepts may be applied to any computing device, processor, or systemcapable of facilitating a telecommunications system. The varioustechniques described herein may be implemented in connection withhardware or software or, where appropriate, with a combination of both.Thus, the methods and devices may take the form of program code (i.e.,instructions) embodied in concrete, tangible, storage media having aconcrete, tangible, physical structure. Examples of tangible storagemedia include floppy diskettes, CD-ROMs, DVDs, hard drives, or any othertangible machine-readable storage medium (computer-readable storagemedium). Thus, a computer-readable storage medium is not a signal. Acomputer-readable storage medium is not a transient signal. Further, acomputer-readable storage medium is not a propagating signal. Acomputer-readable storage medium as described herein is an article ofmanufacture. When the program code is loaded into and executed by amachine, such as a computer, the machine becomes a device fortelecommunications. In the case of program code execution onprogrammable computers, the computing device will generally include aprocessor, a storage medium readable by the processor (includingvolatile or nonvolatile memory or storage elements), at least one inputdevice, and at least one output device. The program(s) can beimplemented in assembly or machine language, if desired. The languagecan be a compiled or interpreted language, and may be combined withhardware implementations.

The methods and devices associated with a telecommunications system asdescribed herein also may be practiced via communications embodied inthe form of program code that is transmitted over some transmissionmedium, such as over electrical wiring or cabling, through fiber optics,or via any other form of transmission, wherein, when the program code isreceived and loaded into and executed by a machine, such as an EPROM, agate array, a programmable logic device (PLD), a client computer, or thelike, the machine becomes an device for implementing telecommunicationsas described herein. When implemented on a general-purpose processor,the program code combines with the processor to provide a unique devicethat operates to invoke the functionality of a telecommunicationssystem.

While a telecommunications system has been described in connection withthe various examples of the various figures, it is to be understood thatother similar implementations may be used or modifications and additionsmay be made to the described examples of a telecommunications systemwithout deviating therefrom. For example, one skilled in the art willrecognize that a telecommunications system as described in the instantapplication may apply to any environment, whether wired or wireless, andmay be applied to any number of such devices connected via acommunications network and interacting across the network. Therefore, atelecommunications system as described herein should not be limited toany single example, but rather should be construed in breadth and scopein accordance with the appended claims.

In describing preferred methods, systems, or apparatuses (e.g., devices)of the subject matter of the present disclosure—multi-layer systemself-optimization—as illustrated in the Figures, specific terminology isemployed for the sake of clarity. The claimed subject matter, however,is not intended to be limited to the specific terminology so selected,and it is to be understood that each specific element includes alltechnical equivalents that operate in a similar manner to accomplish asimilar purpose. In addition, the use of the word “or” is generally usedinclusively unless otherwise provided herein. Real-time as discussedherein refers to operations that usually occur in seconds, but not morethan a minute. As disclosed herein, near real-time events usually occurwithin minutes. A traffic matrix may represent the load from eachingress point to each egress point in an IP network. Although networksare engineered to tolerate some variation in the traffic matrix, largechanges may lead to congested links and poor performance. Configurationchange of a component as disclosed herein may include a software changeor a hardware change (e.g., replace or remove).

This written description uses examples to enable any person skilled inthe art to practice the claimed invention, including making and usingany devices or systems and performing any incorporated methods.

What is claimed:
 1. An apparatus comprising: a processor; and a memorycoupled with the processor, the memory storing executable instructionsthat when executed by the processor cause the processor to effectuateoperations comprising: obtaining multiple layer information associatedwith multiple layers of a telecommunications network, the multiple layerinformation comprising optical layer information or router layerinformation; forecasting a network condition based on the multiple layerinformation, wherein the forecasting is based on machine learning;determining a configuration change of the telecommunications network foravoiding the forecasted network condition; and sending a message to adevice in order to implement the configuration change.
 2. The apparatusof claim 1, wherein the optical layer information is obtained from adynamic fiber cross connect.
 3. The apparatus of claim 1, wherein therouting layer information is obtained from a router.
 4. The apparatus ofclaim 1, wherein the optical layer information comprises a load along anoptical path.
 5. The apparatus of claim 1, wherein the device is acomponent of the optical layer.
 6. The apparatus of claim 1, wherein thedevice is an optical regenerator.
 7. The apparatus of claim 1, whereinthe device is a tail.
 8. The apparatus of claim 1, wherein the device isa reconfigurable optical add-drop multiplexer.
 9. A method comprising:obtaining multiple layer information associated with multiple layers ofa telecommunications network, the multiple layer information comprisingoptical layer information or router layer information; forecasting anetwork condition based on the multiple layer information, wherein theforecasting is based on machine learning; determining a configurationchange of the telecommunications network for avoiding the forecastednetwork condition; and sending a message to a device in order toimplement the configuration change.
 10. The method of claim 9, whereinthe optical layer information is obtained from a dynamic fiber crossconnect.
 11. The method of claim 9, wherein the routing layerinformation is obtained from a router.
 12. The method of claim 9,wherein the optical layer information comprises a load along an opticalpath.
 13. The method of claim 9, wherein the device is a component ofthe optical layer.
 14. The method of claim 9, wherein the device is anoptical regenerator.
 15. The method of claim 9, wherein the device is atail.
 16. A computer readable storage medium that is not a signalstoring computer executable instructions that when executed by acomputing device cause said computing device to effectuate operationscomprising: obtaining multiple layer information associated withmultiple layers of a telecommunications network, the multiple layerinformation comprising optical layer information or router layerinformation; forecasting a network condition based on the multiple layerinformation, wherein the forecasting is based on machine learning;determining a configuration change of the telecommunications network foravoiding the forecasted network condition; and sending a message to adevice in order to implement the configuration change.
 17. The computerreadable storage medium of claim 16, wherein the optical layerinformation is obtained from a dynamic fiber cross connect.
 18. Thecomputer readable storage medium of claim 16, wherein the routing layerinformation is obtained from a router.
 19. The computer readable storagemedium of claim 16, wherein the optical layer information comprises aload along an optical path.
 20. The computer readable storage medium ofclaim 16, wherein the device is a component of the optical layer.